APSIPA Transactions on Signal and Information Processing > Vol 10 > Issue 1

Automatic Deception Detection using Multiple Speech and Language Communicative Descriptors in Dialogs

Huang-Cheng Chou, National Tsing Hua University, Taiwan, hc.chou@gapp.nthu.edu.tw , Yi-Wen Liu, National Tsing Hua University, Taiwan, Chi-Chun Lee, National Tsing Hua University, Taiwan, hc.chou@gapp.nthu.edu.tw
 
Suggested Citation
Huang-Cheng Chou, Yi-Wen Liu and Chi-Chun Lee (2021), "Automatic Deception Detection using Multiple Speech and Language Communicative Descriptors in Dialogs", APSIPA Transactions on Signal and Information Processing: Vol. 10: No. 1, e5. http://dx.doi.org/10.1017/ATSIP.2021.6

Publication Date: 16 Apr 2021
© 2021 Huang-Cheng Chou, Yi-Wen Liu and Chi-Chun Lee
 
Subjects
 
Keywords
Automatic deception detectionImplicature classificationConversational temporal dynamicsBLSTM with hierarchical attention networks
 

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In this article:
I. INTRODUCTION 
II. THE DDDM DATABASE 
III. RESEARCH METHODOLOGY 
IV. EXPERIMENTS 
V. CONCLUSIONS AND FUTURE WORK 

Abstract

While deceptive behaviors are a natural part of human life, it is well known that human is generally bad at detecting deception. In this study, we present an automatic deception detection framework by comprehensively integrating prior domain knowledge in deceptive behavior understanding. Specifically, we compute acoustics, textual information, implicatures with non-verbal behaviors, and conversational temporal dynamics for improving automatic deception detection in dialogs. The proposed model reaches start-of-the-art performance on the Daily Deceptive Dialogues corpus of Mandarin (DDDM) database, 80.61% unweighted accuracy recall in deception recognition. In the further analyses, we reveal that (i) the deceivers’ deception behaviors can be observed from the interrogators’ behaviors in the conversational temporal dynamics features and (ii) some of the acoustic features (e.g. loudness and MFCC) and textual features are significant and effective indicators to detect deception behaviors.

DOI:10.1017/ATSIP.2021.6